Function fdata_to_pc 

pub fn fdata_to_pc(
    data: &FdMatrix,
    ncomp: usize,
    argvals: &[f64],
) -> Result<FpcaResult, FdarError>

Perform functional PCA via SVD on centered data with integration weights.

Uses Simpson’s-rule weights derived from argvals so that the resulting scores represent functional inner products and are invariant to grid density.

Arguments

• data - Matrix (n x m): n observations, m evaluation points
• ncomp - Number of components to extract
• argvals - Evaluation grid points (length m)

Errors

Returns FdarError::InvalidDimension if data has zero rows or zero columns, or if argvals.len() != m. Returns FdarError::InvalidParameter if ncomp is zero. Returns FdarError::ComputationFailed if the SVD decomposition fails to produce U or V_t matrices.

Examples

use fdars_core::matrix::FdMatrix;
use fdars_core::regression::fdata_to_pc;

// 5 curves, each evaluated at 10 points
let data = FdMatrix::from_column_major(
    (0..50).map(|i| (i as f64 * 0.1).sin()).collect(),
    5, 10,
).unwrap();
let argvals: Vec<f64> = (0..10).map(|i| i as f64 / 9.0).collect();
let result = fdata_to_pc(&data, 3, &argvals).unwrap();
assert_eq!(result.scores.shape(), (5, 3));
assert_eq!(result.rotation.shape(), (10, 3));
assert_eq!(result.mean.len(), 10);